Artificial Intelligence and Machine Learning Full Stack
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Course Overview
This comprehensive course is designed to provide hands-on training on artificial intelligence (AI) and machine learning (ML) to individuals and corporate employees, starting from the beginner level and progressing to advanced topics. The course is divided into five levels, with each level building upon the previous level. The course covers a range of topics including programming, statistical data analysis ,machine learning algorithms, deep learning techniques, and natural language processing. Participants will have the opportunity to work on two mini-projects to apply their learning in real-world scenarios.
At the end of the training, participants will be able to:
- Understand the concepts and principles of artificial intelligence and machine learning
- Develop and deploy machine learning models
- Work with deep learning techniques for complex data analysis
- Implement natural language processing in real-world scenarios
- Use programming languages and databases for data analysis
- Perform statistical data analysis for effective decision-making
- Work on mini-projects to apply their learning in real-world scenarios
Pre-requisite
Duration
30 days
Course Outline
- Introduction to programming languages such as Python and their applications in AI and ML
Data types, variables, operators, and functions
Control statements and loops
Introduction to databases and SQL
Data manipulation and management using SQL
Hands-on Labs:
Setting up Python and Jupyter Notebook
Python Basics and Syntax
Working with variables, data types, and operators
Control statements and loops - Introduction to SQL
Data manipulation using SQL
- Descriptive statistics
Probability theory and distributions
Inferential statistics
Hypothesis testing
Regression analysis
Introduction to data visualization
Hands-on Labs:
Descriptive statistics with Python
Probability and distributions with Python
Inferential statistics and hypothesis testing with Python
Regression analysis with Python
Introduction to data visualization with Python
Data visualization with Matplotlib and Seaborn
Mini-Project 1:
Analyze a dataset and provide insights using statistical data analysis and visualization techniques
- Supervised and unsupervised learning
Decision trees and random forests
Linear and logistic regression
Support vector machines
Clustering algorithms
Model selection and evaluation - Introduction to Machine Learning with Python
Supervised Learning – Linear Regression
Supervised Learning – Logistic Regression
Supervised Learning – Decision Trees and Random Forests
Unsupervised Learning – Clustering
Model Evaluation and Selection
Mini-Project 2:
Develop and deploy a machine learning model for a real-world use case
- Neural networks
Convolutional neural networks
Recurrent neural networks
Deep learning frameworks such as TensorFlow and Keras
Hyperparameter tuning
Hands-on Labs:
Introduction to Deep Learning
Neural Networks with TensorFlow and Keras
Convolutional Neural Networks
Recurrent Neural Networks
Hyperparameter Tuning
Transfer Learning
- Text preprocessing and cleaning
Text classification
Named entity recognition
Sentiment analysis - Language translation using neural machine translation
Chatbots and voice assistants
Hands-on lab: Participants will implement natural language processing algorithms using Pythonand libraries such as NLTK and spaCy.
Methodology: The course will be conducted through a mix of theoretical lectures, hands-on practical sessions, case studies, and group discussions .Participants will work on two mini-projects to reinforce their understanding of the concepts covered in the course. Participants will
also have access to a learning management system where they can access course materials, videos,
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